


import argparse
import json
from collections import OrderedDict
from pathlib import Path

import requests
import torch
from huggingface_hub import hf_hub_download
from PIL import Image

from transformers import (
    ConditionalDetrConfig,
    ConditionalDetrForObjectDetection,
    ConditionalDetrForSegmentation,
    ConditionalDetrImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
_SCREAMING_SNAKE_CASE     :					int      =       logging.get_logger(__name__)

# here we list all keys to be renamed (original name on the left, our name on the right)
_SCREAMING_SNAKE_CASE     :					Optional[Any]      =       []
for i in range(6):
 # encoder layers: output projection, 2 feedforward neural networks and 2 layernorms
 rename_keys.append(
     (f'''transformer.encoder.layers.{i}.self_attn.out_proj.weight''', f'''encoder.layers.{i}.self_attn.out_proj.weight''')
 )
 rename_keys.append(
     (f'''transformer.encoder.layers.{i}.self_attn.out_proj.bias''', f'''encoder.layers.{i}.self_attn.out_proj.bias''')
 )
 rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.weight''', f'''encoder.layers.{i}.fc1.weight'''))
 rename_keys.append((f'''transformer.encoder.layers.{i}.linear1.bias''', f'''encoder.layers.{i}.fc1.bias'''))
 rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.weight''', f'''encoder.layers.{i}.fc2.weight'''))
 rename_keys.append((f'''transformer.encoder.layers.{i}.linear2.bias''', f'''encoder.layers.{i}.fc2.bias'''))
 rename_keys.append(
     (f'''transformer.encoder.layers.{i}.norm1.weight''', f'''encoder.layers.{i}.self_attn_layer_norm.weight''')
 )
 rename_keys.append((f'''transformer.encoder.layers.{i}.norm1.bias''', f'''encoder.layers.{i}.self_attn_layer_norm.bias'''))
 rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.weight''', f'''encoder.layers.{i}.final_layer_norm.weight'''))
 rename_keys.append((f'''transformer.encoder.layers.{i}.norm2.bias''', f'''encoder.layers.{i}.final_layer_norm.bias'''))
 # decoder layers: 2 times output projection, 2 feedforward neural networks and 3 layernorms
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.self_attn.out_proj.weight''', f'''decoder.layers.{i}.self_attn.out_proj.weight''')
 )
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.self_attn.out_proj.bias''', f'''decoder.layers.{i}.self_attn.out_proj.bias''')
 )
 rename_keys.append(
     (
         f'''transformer.decoder.layers.{i}.cross_attn.out_proj.weight''',
         f'''decoder.layers.{i}.encoder_attn.out_proj.weight''',
     )
 )
 rename_keys.append(
     (
         f'''transformer.decoder.layers.{i}.cross_attn.out_proj.bias''',
         f'''decoder.layers.{i}.encoder_attn.out_proj.bias''',
     )
 )
 rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.weight''', f'''decoder.layers.{i}.fc1.weight'''))
 rename_keys.append((f'''transformer.decoder.layers.{i}.linear1.bias''', f'''decoder.layers.{i}.fc1.bias'''))
 rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.weight''', f'''decoder.layers.{i}.fc2.weight'''))
 rename_keys.append((f'''transformer.decoder.layers.{i}.linear2.bias''', f'''decoder.layers.{i}.fc2.bias'''))
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.norm1.weight''', f'''decoder.layers.{i}.self_attn_layer_norm.weight''')
 )
 rename_keys.append((f'''transformer.decoder.layers.{i}.norm1.bias''', f'''decoder.layers.{i}.self_attn_layer_norm.bias'''))
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.norm2.weight''', f'''decoder.layers.{i}.encoder_attn_layer_norm.weight''')
 )
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.norm2.bias''', f'''decoder.layers.{i}.encoder_attn_layer_norm.bias''')
 )
 rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.weight''', f'''decoder.layers.{i}.final_layer_norm.weight'''))
 rename_keys.append((f'''transformer.decoder.layers.{i}.norm3.bias''', f'''decoder.layers.{i}.final_layer_norm.bias'''))

 # q, k, v projections in self/cross-attention in decoder for conditional DETR
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.weight''', f'''decoder.layers.{i}.sa_qcontent_proj.weight''')
 )
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.weight''', f'''decoder.layers.{i}.sa_kcontent_proj.weight''')
 )
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.sa_qpos_proj.weight''', f'''decoder.layers.{i}.sa_qpos_proj.weight''')
 )
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.sa_kpos_proj.weight''', f'''decoder.layers.{i}.sa_kpos_proj.weight''')
 )
 rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.weight''', f'''decoder.layers.{i}.sa_v_proj.weight'''))
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.weight''', f'''decoder.layers.{i}.ca_qcontent_proj.weight''')
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.weight", f"decoder.layers.{i}.ca_qpos_proj.weight"))
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.weight''', f'''decoder.layers.{i}.ca_kcontent_proj.weight''')
 )
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.ca_kpos_proj.weight''', f'''decoder.layers.{i}.ca_kpos_proj.weight''')
 )
 rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.weight''', f'''decoder.layers.{i}.ca_v_proj.weight'''))
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.weight''', f'''decoder.layers.{i}.ca_qpos_sine_proj.weight''')
 )

 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.sa_qcontent_proj.bias''', f'''decoder.layers.{i}.sa_qcontent_proj.bias''')
 )
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.sa_kcontent_proj.bias''', f'''decoder.layers.{i}.sa_kcontent_proj.bias''')
 )
 rename_keys.append((f'''transformer.decoder.layers.{i}.sa_qpos_proj.bias''', f'''decoder.layers.{i}.sa_qpos_proj.bias'''))
 rename_keys.append((f'''transformer.decoder.layers.{i}.sa_kpos_proj.bias''', f'''decoder.layers.{i}.sa_kpos_proj.bias'''))
 rename_keys.append((f'''transformer.decoder.layers.{i}.sa_v_proj.bias''', f'''decoder.layers.{i}.sa_v_proj.bias'''))
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.ca_qcontent_proj.bias''', f'''decoder.layers.{i}.ca_qcontent_proj.bias''')
 )
 # rename_keys.append((f"transformer.decoder.layers.{i}.ca_qpos_proj.bias", f"decoder.layers.{i}.ca_qpos_proj.bias"))
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.ca_kcontent_proj.bias''', f'''decoder.layers.{i}.ca_kcontent_proj.bias''')
 )
 rename_keys.append((f'''transformer.decoder.layers.{i}.ca_kpos_proj.bias''', f'''decoder.layers.{i}.ca_kpos_proj.bias'''))
 rename_keys.append((f'''transformer.decoder.layers.{i}.ca_v_proj.bias''', f'''decoder.layers.{i}.ca_v_proj.bias'''))
 rename_keys.append(
     (f'''transformer.decoder.layers.{i}.ca_qpos_sine_proj.bias''', f'''decoder.layers.{i}.ca_qpos_sine_proj.bias''')
 )

# convolutional projection + query embeddings + layernorm of decoder + class and bounding box heads
# for conditional DETR, also convert reference point head and query scale MLP
rename_keys.extend(
    [
        ("input_proj.weight", "input_projection.weight"),
        ("input_proj.bias", "input_projection.bias"),
        ("query_embed.weight", "query_position_embeddings.weight"),
        ("transformer.decoder.norm.weight", "decoder.layernorm.weight"),
        ("transformer.decoder.norm.bias", "decoder.layernorm.bias"),
        ("class_embed.weight", "class_labels_classifier.weight"),
        ("class_embed.bias", "class_labels_classifier.bias"),
        ("bbox_embed.layers.0.weight", "bbox_predictor.layers.0.weight"),
        ("bbox_embed.layers.0.bias", "bbox_predictor.layers.0.bias"),
        ("bbox_embed.layers.1.weight", "bbox_predictor.layers.1.weight"),
        ("bbox_embed.layers.1.bias", "bbox_predictor.layers.1.bias"),
        ("bbox_embed.layers.2.weight", "bbox_predictor.layers.2.weight"),
        ("bbox_embed.layers.2.bias", "bbox_predictor.layers.2.bias"),
        ("transformer.decoder.ref_point_head.layers.0.weight", "decoder.ref_point_head.layers.0.weight"),
        ("transformer.decoder.ref_point_head.layers.0.bias", "decoder.ref_point_head.layers.0.bias"),
        ("transformer.decoder.ref_point_head.layers.1.weight", "decoder.ref_point_head.layers.1.weight"),
        ("transformer.decoder.ref_point_head.layers.1.bias", "decoder.ref_point_head.layers.1.bias"),
        ("transformer.decoder.query_scale.layers.0.weight", "decoder.query_scale.layers.0.weight"),
        ("transformer.decoder.query_scale.layers.0.bias", "decoder.query_scale.layers.0.bias"),
        ("transformer.decoder.query_scale.layers.1.weight", "decoder.query_scale.layers.1.weight"),
        ("transformer.decoder.query_scale.layers.1.bias", "decoder.query_scale.layers.1.bias"),
        ("transformer.decoder.layers.0.ca_qpos_proj.weight", "decoder.layers.0.ca_qpos_proj.weight"),
        ("transformer.decoder.layers.0.ca_qpos_proj.bias", "decoder.layers.0.ca_qpos_proj.bias"),
    ]
)
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							state_dict.pop(UpperCamelCase_							)
     snake_case											=							val
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							OrderedDict()
     for key, value in state_dict.items():
          if "backbone.0.body" in key:
               snake_case											=							key.replace('''backbone.0.body'''				,'''backbone.conv_encoder.model'''							)
               snake_case											=							value
          else:
               snake_case											=							value

     return new_state_dict
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_=False							):



     """simple docstring"""






     snake_case											=							''''''
     if is_panoptic:
          snake_case											=							'''conditional_detr.'''

     # first: transformer encoder
     for i in range(6							):
          # read in weights + bias of input projection layer (in PyTorch's MultiHeadAttention, this is a single matrix + bias)
          snake_case											=							state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_weight'''							)
          snake_case											=							state_dict.pop(F'''{prefix}transformer.encoder.layers.{i}.self_attn.in_proj_bias'''							)
          # next, add query, keys and values (in that order) to the state dict
          snake_case											=							in_proj_weight[:2_56, :]
          snake_case											=							in_proj_bias[:2_56]
          snake_case											=							in_proj_weight[2_56:5_12, :]
          snake_case											=							in_proj_bias[2_56:5_12]
          snake_case											=							in_proj_weight[-2_56:, :]
          snake_case											=							in_proj_bias[-2_56:]
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							'''http://images.cocodataset.org/val2017/000000039769.jpg'''
     snake_case											=							Image.open(requests.get(UpperCamelCase_				,stream=UpperCamelCase_							).raw							)

     return im
@torch.no_grad()
def      UpperCAmelCase__		(UpperCamelCase_				,UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							ConditionalDetrConfig()
     # set backbone and dilation attributes
     if "resnet101" in model_name:
          snake_case											=							'''resnet101'''
     if "dc5" in model_name:
          snake_case											=							True
     snake_case											=							'''panoptic''' in model_name
     if is_panoptic:
          snake_case											=							2_50
     else:
          snake_case											=							91
          snake_case											=							'''huggingface/label-files'''
          snake_case											=							'''coco-detection-id2label.json'''
          snake_case											=							json.load(open(hf_hub_download(UpperCamelCase_				,UpperCamelCase_				,repo_type='''dataset'''							)				,'''r'''							)							)
          snake_case											=							{int(UpperCamelCase_							): v for k, v in idalabel.items()}
          snake_case											=							idalabel
          snake_case											=							{v: k for k, v in idalabel.items()}

     # load image processor
     snake_case											=							'''coco_panoptic''' if is_panoptic else '''coco_detection'''
     snake_case											=							ConditionalDetrImageProcessor(format=UpperCamelCase_							)

     # prepare image
     snake_case											=							prepare_img()
     snake_case											=							image_processor(images=UpperCamelCase_				,return_tensors='''pt'''							)
     snake_case											=							encoding['''pixel_values''']

     logger.info(F'''Converting model {model_name}...'''							)

     # load original model from torch hub
     snake_case											=							torch.hub.load('''DeppMeng/ConditionalDETR'''				,UpperCamelCase_				,pretrained=UpperCamelCase_							).eval()
     snake_case											=							conditional_detr.state_dict()
     # rename keys
     for src, dest in rename_keys:
          if is_panoptic:
               snake_case											=							'''conditional_detr.''' + src
          rename_key(UpperCamelCase_				,UpperCamelCase_				,UpperCamelCase_							)
     snake_case											=							rename_backbone_keys(UpperCamelCase_							)
     # query, key and value matrices need special treatment
     read_in_q_k_v(UpperCamelCase_				,is_panoptic=UpperCamelCase_							)
     # important: we need to prepend a prefix to each of the base model keys as the head models use different attributes for them
     snake_case											=							'''conditional_detr.model.''' if is_panoptic else '''model.'''
     for key in state_dict.copy().keys():
          if is_panoptic:
               if (
                   key.startswith('''conditional_detr'''							)
                   and not key.startswith('''class_labels_classifier'''							)
                   and not key.startswith('''bbox_predictor'''							)
               ):
                    snake_case											=							state_dict.pop(UpperCamelCase_							)
                    snake_case											=							val
               elif "class_labels_classifier" in key or "bbox_predictor" in key:
                    snake_case											=							state_dict.pop(UpperCamelCase_							)
                    snake_case											=							val
               elif key.startswith('''bbox_attention'''							) or key.startswith('''mask_head'''							):
                    continue
               else:
                    snake_case											=							state_dict.pop(UpperCamelCase_							)
                    snake_case											=							val
          else:
               if not key.startswith('''class_labels_classifier'''							) and not key.startswith('''bbox_predictor'''							):
                    snake_case											=							state_dict.pop(UpperCamelCase_							)
                    snake_case											=							val
    # finally, create HuggingFace model and load state dict
     snake_case											=							ConditionalDetrForSegmentation(UpperCamelCase_							) if is_panoptic else ConditionalDetrForObjectDetection(UpperCamelCase_							)
     model.load_state_dict(UpperCamelCase_							)
     model.eval()
     model.push_to_hub(repo_id=UpperCamelCase_				,organization='''DepuMeng'''				,commit_message='''Add model'''							)
     # verify our conversion
     snake_case											=							conditional_detr(UpperCamelCase_							)
     snake_case											=							model(UpperCamelCase_							)
     assert torch.allclose(outputs.logits				,original_outputs['''pred_logits''']				,atol=1e-4							)
     assert torch.allclose(outputs.pred_boxes				,original_outputs['''pred_boxes''']				,atol=1e-4							)
     if is_panoptic:
          assert torch.allclose(outputs.pred_masks				,original_outputs['''pred_masks''']				,atol=1e-4							)

     # Save model and image processor
     logger.info(F'''Saving PyTorch model and image processor to {pytorch_dump_folder_path}...'''							)
     Path(UpperCamelCase_							).mkdir(exist_ok=UpperCamelCase_							)
     model.save_pretrained(UpperCamelCase_							)
     image_processor.save_pretrained(UpperCamelCase_							)


if __name__ == "__main__":
 _SCREAMING_SNAKE_CASE     :					Any      =       argparse.ArgumentParser()

 parser.add_argument(
     "--model_name",
     default="conditional_detr_resnet50",
     type=str,
     help="Name of the CONDITIONAL_DETR model you'd like to convert.",
 )
 parser.add_argument(
     "--pytorch_dump_folder_path", default=None, type=str, help="Path to the folder to output PyTorch model."
 )
 _SCREAMING_SNAKE_CASE     :					Tuple      =       parser.parse_args()
 convert_conditional_detr_checkpoint(args.model_name, args.pytorch_dump_folder_path)








import json
import os
from functools import lru_cache
from typing import List, Optional, Tuple

import regex as re

from ...tokenization_utils import AddedToken, PreTrainedTokenizer
from ...utils import logging


_SCREAMING_SNAKE_CASE     :					Any      =       logging.get_logger(__name__)


_SCREAMING_SNAKE_CASE     :					str      =       {"vocab_file": "vocab.json", "merges_file": "merges.txt"}

_SCREAMING_SNAKE_CASE     :					Any      =       {
    "vocab_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/vocab.json",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/vocab.json"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/vocab.json"
        ),
    },
    "merges_file": {
        "allenai/longformer-base-4096": "https://huggingface.co/allenai/longformer-base-4096/resolve/main/merges.txt",
        "allenai/longformer-large-4096": (
            "https://huggingface.co/allenai/longformer-large-4096/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-finetuned-triviaqa": (
            "https://huggingface.co/allenai/longformer-large-4096-finetuned-triviaqa/resolve/main/merges.txt"
        ),
        "allenai/longformer-base-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-base-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
        "allenai/longformer-large-4096-extra.pos.embd.only": (
            "https://huggingface.co/allenai/longformer-large-4096-extra.pos.embd.only/resolve/main/merges.txt"
        ),
    },
}

_SCREAMING_SNAKE_CASE     :					List[Any]      =       {
    "allenai/longformer-base-4096": 40_96,
    "allenai/longformer-large-4096": 40_96,
    "allenai/longformer-large-4096-finetuned-triviaqa": 40_96,
    "allenai/longformer-base-4096-extra.pos.embd.only": 40_96,
    "allenai/longformer-large-4096-extra.pos.embd.only": 40_96,
}
@lru_cache()
# Copied from transformers.models.roberta.tokenization_roberta.bytes_to_unicode
def      UpperCAmelCase__		():



     """simple docstring"""






     snake_case											=							(
         list(range(ord('''!'''							)				,ord('''~'''							) + 1							)							) + list(range(ord('''¡'''							)				,ord('''¬'''							) + 1							)							) + list(range(ord('''®'''							)				,ord('''ÿ'''							) + 1							)							)
     )
     snake_case											=							bs[:]
     snake_case											=							0
     for b in range(2**8							):
          if b not in bs:
               bs.append(UpperCamelCase_							)
               cs.append(2**8 + n							)
               n += 1
     snake_case											=							[chr(UpperCamelCase_							) for n in cs]
     return dict(zip(UpperCamelCase_				,UpperCamelCase_							)							)
def      UpperCAmelCase__		(UpperCamelCase_							):



     """simple docstring"""






     snake_case											=							set()
     snake_case											=							word[0]
     for char in word[1:]:
          pairs.add((prev_char, char)							)
          snake_case											=							char
     return pairs

class     A__   (       snake_case__      ):







    """simple docstring"""

    __magic_name__         =						VOCAB_FILES_NAMES
    __magic_name__         =						PRETRAINED_VOCAB_FILES_MAP
    __magic_name__         =						PRETRAINED_POSITIONAL_EMBEDDINGS_SIZES
    __magic_name__         =						['input_ids', 'attention_mask']
    def __init__(			self     ,   __snake_case     ,   __snake_case     ,   __snake_case="replace"     ,   __snake_case="<s>"     ,   __snake_case="</s>"     ,   __snake_case="</s>"     ,   __snake_case="<s>"     ,   __snake_case="<unk>"     ,   __snake_case="<pad>"     ,   __snake_case="<mask>"     ,   __snake_case=False     ,   **__snake_case     ,   ):
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else bos_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else eos_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else sep_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else cls_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else unk_token
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else pad_token

         # Mask token behave like a normal word, i.e. include the space before it
         snake_case											=							AddedToken(__snake_case     ,   lstrip=__snake_case     ,   rstrip=__snake_case       ) if isinstance(__snake_case     ,   __snake_case       ) else mask_token

         super().__init__(
             errors=__snake_case     ,   bos_token=__snake_case     ,   eos_token=__snake_case     ,   unk_token=__snake_case     ,   sep_token=__snake_case     ,   cls_token=__snake_case     ,   pad_token=__snake_case     ,   mask_token=__snake_case     ,   add_prefix_space=__snake_case     ,   **__snake_case     ,   )

         with open(__snake_case     ,   encoding='''utf-8'''       ) as vocab_handle:
              snake_case											=							json.load(__snake_case       )
         snake_case											=							{v: k for k, v in self.encoder.items()}
         snake_case											=							errors  # how to handle errors in decoding
         snake_case											=							bytes_to_unicode()
         snake_case											=							{v: k for k, v in self.byte_encoder.items()}
         with open(__snake_case     ,   encoding='''utf-8'''       ) as merges_handle:
              snake_case											=							merges_handle.read().split('''\n'''       )[1:-1]
         snake_case											=							[tuple(merge.split()       ) for merge in bpe_merges]
         snake_case											=							dict(zip(__snake_case     ,   range(len(__snake_case       )       )       )       )
         snake_case											=							{}
         snake_case											=							add_prefix_space

         # Should have added re.IGNORECASE so BPE merges can happen for capitalized versions of contractions
         snake_case											=							re.compile(R'''\'s|\'t|\'re|\'ve|\'m|\'ll|\'d| ?\p{L}+| ?\p{N}+| ?[^\s\p{L}\p{N}]+|\s+(?!\S)|\s+'''       )
    @property
    def 						a_     (			self       ):
         return len(self.encoder       )
    def 						a_     (			self       ):
         return dict(self.encoder     ,   **self.added_tokens_encoder       )
    def 						a_     (			self     ,   __snake_case       ):
         if token in self.cache:
              return self.cache[token]
         snake_case											=							tuple(__snake_case       )
         snake_case											=							get_pairs(__snake_case       )

         if not pairs:
              return token

         while True:
              snake_case											=							min(__snake_case     ,   key=lambda __snake_case       : self.bpe_ranks.get(__snake_case     ,   float('''inf'''       )       )       )
              if bigram not in self.bpe_ranks:
                   break
              snake_case					,    snake_case											=							bigram
              snake_case											=							[]
              snake_case											=							0
              while i < len(__snake_case       ):
                   try:
                        snake_case											=							word.index(__snake_case     ,   __snake_case       )
                   except ValueError:
                        new_word.extend(word[i:]       )
                        break
                   else:
                        new_word.extend(word[i:j]       )
                        snake_case											=							j

                   if word[i] == first and i < len(__snake_case       ) - 1 and word[i + 1] == second:
                        new_word.append(first + second       )
                        i += 2
                   else:
                        new_word.append(word[i]       )
                        i += 1
              snake_case											=							tuple(__snake_case       )
              snake_case											=							new_word
              if len(__snake_case       ) == 1:
                   break
              else:
                   snake_case											=							get_pairs(__snake_case       )
         snake_case											=							''' '''.join(__snake_case       )
         snake_case											=							word
         return word
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							[]
         for token in re.findall(self.pat     ,   __snake_case       ):
              snake_case											=							''''''.join(
                  self.byte_encoder[b] for b in token.encode('''utf-8'''       )       )  # Maps all our bytes to unicode strings, avoiding control tokens of the BPE (spaces in our case)
              bpe_tokens.extend(bpe_token for bpe_token in self.bpe(__snake_case       ).split(''' '''       )       )
         return bpe_tokens
    def 						a_     (			self     ,   __snake_case       ):
         return self.encoder.get(__snake_case     ,   self.encoder.get(self.unk_token       )       )
    def 						a_     (			self     ,   __snake_case       ):
         return self.decoder.get(__snake_case       )
    def 						a_     (			self     ,   __snake_case       ):
         snake_case											=							''''''.join(__snake_case       )
         snake_case											=							bytearray([self.byte_decoder[c] for c in text]       ).decode('''utf-8'''     ,   errors=self.errors       )
         return text
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         if not os.path.isdir(__snake_case       ):
              logger.error(F'''Vocabulary path ({save_directory}) should be a directory'''       )
              return
         snake_case											=							os.path.join(
             __snake_case     ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''vocab_file''']       )
         snake_case											=							os.path.join(
             __snake_case     ,   (filename_prefix + '''-''' if filename_prefix else '''''') + VOCAB_FILES_NAMES['''merges_file''']       )

         with open(__snake_case     ,   '''w'''     ,   encoding='''utf-8'''       ) as f:
              f.write(json.dumps(self.encoder     ,   indent=2     ,   sort_keys=__snake_case     ,   ensure_ascii=__snake_case       ) + '''\n'''       )

         snake_case											=							0
         with open(__snake_case     ,   '''w'''     ,   encoding='''utf-8'''       ) as writer:
              writer.write('''#version: 0.2\n'''       )
              for bpe_tokens, token_index in sorted(self.bpe_ranks.items()     ,   key=lambda __snake_case       : kv[1]       ):
                   if index != token_index:
                        logger.warning(
                            F'''Saving vocabulary to {merge_file}: BPE merge indices are not consecutive.'''
                            ''' Please check that the tokenizer is not corrupted!'''       )
                        snake_case											=							token_index
                   writer.write(''' '''.join(__snake_case       ) + '''\n'''       )
                   index += 1

         return vocab_file, merge_file
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         if token_ids_a is None:
              return [self.cls_token_id] + token_ids_a + [self.sep_token_id]
         snake_case											=							[self.cls_token_id]
         snake_case											=							[self.sep_token_id]
         return cls + token_ids_a + sep + sep + token_ids_a + sep
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None     ,   __snake_case = False       ):
         if already_has_special_tokens:
              return super().get_special_tokens_mask(
                  token_ids_a=__snake_case     ,   token_ids_a=__snake_case     ,   already_has_special_tokens=__snake_case       )

         if token_ids_a is None:
              return [1] + ([0] * len(__snake_case       )) + [1]
         return [1] + ([0] * len(__snake_case       )) + [1, 1] + ([0] * len(__snake_case       )) + [1]
    def 						a_     (			self     ,   __snake_case     ,   __snake_case = None       ):
         snake_case											=							[self.sep_token_id]
         snake_case											=							[self.cls_token_id]

         if token_ids_a is None:
              return len(cls + token_ids_a + sep       ) * [0]
         return len(cls + token_ids_a + sep + sep + token_ids_a + sep       ) * [0]




    def 						a_     (			self     ,   __snake_case     ,   __snake_case=False     ,   **__snake_case       ):
         snake_case											=							kwargs.pop('''add_prefix_space'''     ,   self.add_prefix_space       )
         if (is_split_into_words or add_prefix_space) and (len(__snake_case       ) > 0 and not text[0].isspace()):
              snake_case											=							''' ''' + text
         return (text, kwargs)




